Pharmacokinetic/Pharmacodynamic Modeling and Application in Antibacterial and Antifungal Pharmacotherapy: A Narrative Review
Abstract
:1. Introduction
2. Basic Aspects of PK/PD Modeling
3. Use of PK/PD Models with Antimicrobials
3.1. MIC-Based Approach
3.2. Use of Time-Kill Curves
3.3. In Vitro Pharmacodynamic (PD) Models
4. Studies with PK/PD Models in Antibacterial and Antifungal
4.1. Antibacterials
4.1.1. Beta-Lactams
Penicillins
Cephalosporins
Carbapenems
4.1.2. Aminoglycosides
4.1.3. Macrolides
4.1.4. Rifamycins
4.1.5. Oxazolidinones
4.1.6. Fluoroquinolones
4.1.7. Polymyxins
4.1.8. Glycopeptides
4.1.9. Tetracyclines
4.2. Antifungals
5. The Use of Pop PK/PD Models
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Mohr, K.I. History of Antibiotics Research. In How to Overcome the Antibiotic Crisis; Current Topics in Microbiology and, Immunology; Stadler, M., Dersch, P., Eds.; Springer International Publishing: Cham, Switzerland, 2016; Volume 398, pp. 237–272. Available online: http://link.springer.com/10.1007/82_2016_499 (accessed on 31 August 2021).
- Folgori, L.; Bernaschi, P.; Piga, S.; Carletti, M.; Cunha, F.P.; Lara, P.H.R.; de Castro Peixoto, N.C.; Guimarães, B.G.A.; Sharland, M.; Da Silva, A.R.A.; et al. Healthcare-Associated Infections in Pediatric and Neonatal Intensive Care Units: Impact of Underlying Risk Factors and Antimicrobial Resistance on 30-Day Case-Fatality in Italy and Brazil. Infect. Control Hosp. Epidemiol. 2016, 37, 1302–1309. [Google Scholar] [CrossRef] [Green Version]
- Braga, I.A.; Campos, P.A.; Gontijo-Filho, P.P.; Ribas, R.M. Multi-hospital point prevalence study of healthcare-associated infections in 28 adult intensive care units in Brazil. J. Hosp. Infect. 2018, 99, 318–324. [Google Scholar] [CrossRef]
- Breijyeh, Z.; Jubeh, B.; Karaman, R. Resistance of Gram-Negative Bacteria to Current Antibacterial Agents and Approaches to Resolve It. Molecules 2020, 25, 1340. [Google Scholar] [CrossRef] [Green Version]
- Li, Y.; Sun, L.; Lu, C.; Gong, Y.; Li, M.; Sun, S. Promising Antifungal Targets Against Candida albicans Based on Ion Homeostasis. Front. Cell. Infect. Microbiol. 2018, 8, 286. [Google Scholar] [CrossRef] [PubMed]
- Sierra, J.M.; Fusté, E.; Rabanal, F.; Vinuesa, T.; Viñas, M. An overview of antimicrobial peptides and the latest advances in their development. Expert Opin. Biol. Ther. 2017, 17, 663–676. [Google Scholar] [CrossRef] [PubMed]
- Wood, J.B.; Cravens, L.B.; Creech, C.B. Advances in pediatric antimicrobial agents development. Curr. Opin. Pediatrics 2019, 31, 135–143. [Google Scholar] [CrossRef]
- Kang, J.S.; Lee, M.H. Overview of therapeutic drug monitoring. Korean J. Intern. Med. 2009, 24, 1–10. [Google Scholar] [CrossRef] [PubMed]
- Lin, J.H.; Lu, A.Y. Role of pharmacokinetics and metabolism in drug discovery and development. Pharmacol. Rev. 1997, 49, 403–449. [Google Scholar] [PubMed]
- Gross, A.S. Best practice in therapeutic drug monitoring. Br. J. Clin. Pharmacol. 2001, 52 (Suppl. S1), 5–9. [Google Scholar]
- Derendorf, H.; Meibohm, B. Modeling of pharmacokinetic/pharmacodynamic (PK/PD) relationships: Concepts and perspectives. Pharm. Res. 1999, 16, 176–185. [Google Scholar] [CrossRef]
- Holford, N.H.; Sheiner, L.B. Kinetics of pharmacologic response. Pharm. Ther. 1982, 16, 143–166. [Google Scholar] [CrossRef]
- Meibohm, B.; Derendorf, H. Pharmacokinetic/pharmacodynamic studies in drug product development. J. Pharm. Sci. 2002, 91, 18–31. [Google Scholar] [CrossRef] [PubMed]
- Zou, H.; Banerjee, P.; Leung, S.S.Y.; Yan, X. Application of Pharmacokinetic-Pharmacodynamic Modeling in Drug Delivery: Development and Challenges. Front. Pharmacol. 2020, 11, 997. [Google Scholar] [CrossRef] [PubMed]
- Schuck, E.L.; Derendorf, H. Pharmacokinetic/ pharmacodynamic evaluation of anti-infective agents. Expert Rev. Anti-Infect. Ther. 2005, 3, 361–373. [Google Scholar] [CrossRef] [PubMed]
- Onufrak, N.J.; Forrest, A.; Gonzalez, D. Pharmacokinetic and Pharmacodynamic Principles of Anti-infective Dosing. Clin. Ther. 2016, 38, 1930–1947. [Google Scholar] [CrossRef] [Green Version]
- Velkov, T.; Bergen, P.J.; Lora-Tamayo, J.; Landersdorfer, C.B.; Li, J. PK/PD models in antibacterial development. Curr. Opin. Microbiol. 2013, 16, 573–579. [Google Scholar] [CrossRef] [Green Version]
- Tängdén, T.; Lundberg, C.V.; Friberg, L.E.; Huttner, A. How preclinical infection models help define antibiotic doses in the clinic. Int. J. Antimicrob. Agents 2020, 56, 106008. [Google Scholar] [CrossRef]
- Meibohm, B.; Derendorf, H. Basic concepts of pharmacokinetic/pharmacodynamic (PK/PD) modelling. Int. J. Clin. Pharm. Ther. 1997, 35, 401–413. [Google Scholar]
- Chiann, C.; Rama, E.; Cristofoletti, R. Técnicas Computacionais em Farmacocinética. In Farmacocinética Básica e Aplicada; Guanabara Koogan Ltda.: Rio de Janeiro, Brazil, 2011; pp. 162–172. [Google Scholar]
- Rosenbaum, S. Basic Pharmacokinetics and Pharmacodynamics: An Integrated Textbook and Computer Simulations, 1st ed.; John Wiley & Sons: Hoboken, NJ, USA, 2011. [Google Scholar]
- Tozer, T.N.; Rowland, M. Introdução à Farmacocinética e à Farmacodinâmica—As Bases Quantitativas da Terapia Farmacológica, 1st ed.; Artmed: Porto Alegre, RS, Brazil, 2009. [Google Scholar]
- Rizk, M.; Zou, L.; Savic, R.; Dooley, K. Importance of Drug Pharmacokinetics at the Site of Action. Clin. Transl. Sci. 2017, 10, 133–142. [Google Scholar] [CrossRef]
- Schmidt, S.; Schuck, E.; Kumar, V.; Burkhardt, O.; Derendorf, H. Integration of pharmacokinetic/pharmacodynamic modeling and simulation in the development of new anti-infective agents—Minimum inhibitory concentration versus time-kill curves. Expert Opin. Drug Discov. 2007, 2, 849–860. [Google Scholar] [CrossRef]
- Asín-Prieto, E.; Rodríguez-Gascón, A.; Isla, A. Applications of the pharmacokinetic/pharmacodynamic (PK/PD) analysis of antimicrobial agents. J. Infect. Chemother. 2015, 21, 319–329. [Google Scholar] [CrossRef] [PubMed]
- Clinical and Laboratory Standards Institute. Methods for Dilution Antimicrobial Susceptibility Tests for Bacteria that Grow Aerobically: M07-A10, 10th ed.; Approved Standard; Committee for Clinical Laboratory Standards: Wayne, PA, USA, 2015; 92p. [Google Scholar]
- Datta Sumi, C.; Heffernan, A.J.; Lipman, J.; Roberts, J.A.; Sime, F.B. What Antibiotic Exposures Are Required to Suppress the Emergence of Resistance for Gram-Negative Bacteria? A Systematic Review. Clin. Pharmacokinet. 2019, 58, 1407–1443. [Google Scholar] [CrossRef] [PubMed]
- Craig, W. Pharmacodynamics of Antimicrobials: General Concepts and Applications. In Antimicrobial Pharmacodynamics in Theory and Clinical Practice; CRC Press: Boca Raton, FL, USA, 2007; Volume 44, pp. 1–22. [Google Scholar]
- Mouton, J.W.; Dudley, M.N.; Cars, O.; Derendorf, H.; Drusano, G.L. Standardization of pharmacokinetic/pharmacodynamic (PK/PD) terminology for anti-infective drugs: An update. J. Antimicrob. Chemother. 2005, 55, 601–607. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Wu, B.; Sy, S.K.B.; Derendorf, H. Principles of Applied Pharmacokinetic–Pharmacodynamic Modeling. In Fundamentals of Antimicrobial Pharmacokinetics and Pharmacodynamics; Vinks, A.A., Derendorf, H., Mouton, J.W., Eds.; Springer: New York, NY, USA, 2014; pp. 63–79. [Google Scholar] [CrossRef]
- Mueller, M.; de la Peña, A.; Derendorf, H. Issues in pharmacokinetics and pharmacodynamics of anti-infective agents: kill curves versus MIC. Antimicrob. Agents Chemother. 2004, 48, 369–377. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Griffith, D.; Dudley, M.N. Animal Models of Infection for the Study of Antibiotic Pharmacodynamics. In Antimicrobial Pharmacodynamics in Theory and Clinical Practice, 2nd ed.; Nightingale, C.H., Ambrose, P.G., Drusano, G.L., Murakawa, T., Eds.; Informa Healthcare USA: New York, NY, USA, 2007; pp. 79–102. [Google Scholar]
- Nolting, A.; Dalla Costa, T.; Rand, K.H.; Derendorf, H. Pharmacokinetic-pharmacodynamic modeling of the antibiotic effect of piperacillin in vitro. Pharm. Res. 1996, 13, 91–96. [Google Scholar] [CrossRef]
- Mouton, J.W.; Vinks, A.A.; Punt, N.C. Pharmacokinetic-pharmacodynamic modeling of activity of ceftazidime during continuous and intermittent infusion. Antimicrob. Agents Chemother. 1997, 41, 733–738. [Google Scholar] [CrossRef] [Green Version]
- Treyaprasert, W.; Schmidt, S.; Rand, K.H.; Suvanakoot, U.; Derensdorf, H. Pharmacokinetic/pharmacodynamic modeling of in vitro activity of azithromycin against four different bacterial strains. Int. J. Antimicrob. Agents 2007, 29, 263–270. [Google Scholar] [CrossRef]
- Nielsen, E.I.; Viberg, A.; Löwdin, E.; Cars, O.; Karlsson, M.O.; Sandström, M. Semimechanistic pharmacokinetic/pharmacodynamic model for assessment of activity of antibacterial agents from time-kill curve experiments. Antimicrob. Agents Chemother. 2007, 51, 128–136. [Google Scholar] [CrossRef] [Green Version]
- Barclay, M.L.; Begg, E.J. Aminoglycoside adaptive resistance: Importance for effective dosage regimens. Drugs 2001, 61, 713–721. [Google Scholar] [CrossRef]
- Mohamed, A.F.; Nielsen, E.I.; Cars, O.; Friberg, L.E. Pharmacokinetic-Pharmacodynamic Model for Gentamicin and Its Adaptive Resistance with Predictions of Dosing Schedules in Newborn Infants. Antimicrob. Agents Chemother. 2012, 56, 179–188. [Google Scholar] [CrossRef] [Green Version]
- Michael, J.; Barth, A.; Kloft, C.; Derendorf, H. Pharmacodynamic In Vitro Models to Determine the Effect of Antibiotics. In Fundamentals of Antimicrobial Pharmacokinetics and Pharmacodynamics; Springer: Heidelberg, Germany, 2014; pp. 81–112. [Google Scholar]
- Gloede, J.; Scheerans, C.; Derendorf, H.; Kloft, C. In vitro pharmacodynamic models to determine the effect of antibacterial drugs. J. Antimicrob. Chemother. 2010, 65, 186–201. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- De Araujo, B.V.; Diniz, A.; Palma, E.C.; Buffé, C.; Costa, T.D. PK-PD modeling of β-lactam antibiotics: In vitro or in vivo models? J. Antibiot. 2011, 64, 439–446. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Bergen, P.J.; Bulitta, J.B.; Kirkpatrick, C.M.; Rogers, K.E.; McGregor, M.J.; Wallis, S.C.; Paterson, D.L.; Nation, R.L.; Lipman, J.; Roberts, J.A.; et al. Substantial Impact of Altered Pharmacokinetics in Critically Ill Patients on the Antibacterial Effects of Meropenem Evaluated via the Dynamic Hollow-Fiber Infection Model. Antimicrob. Agents Chemother. 2017, 61, e02642-16. [Google Scholar] [CrossRef] [Green Version]
- Matsumoto, K.; Sugano, T.; Sato, N.; Ida, T.; Shibasaki, S. Prediction of clinical bacteriological efficacy of oral antibiotics using a mechanism-based pharmacokinetic- pharmacodynamics modeling. Jpn. J. Antibiotics. 2014, 67, 33–47. [Google Scholar]
- De la Peña, A.; Gräbe, A.; Rand, K.H.; Rehak, E.; Gross, J.; Thyroff-Friesinger, U.; Müller, M.; Derendorf, H. PK–PD modelling of the effect of cefaclor on four different bacterial strains. Int. J. Antimicrob. Agents. 2004, 23, 218–225. [Google Scholar] [CrossRef] [PubMed]
- Zhuang, L.; He, Y.; Xia, H.; Liu, Y.; Sy, S.K.B.; Derendorf, H. Gentamicin dosing strategy in patients with end-stage renal disease receiving haemodialysis: Evaluation using a semi-mechanistic pharmacokinetic/pharmacodynamic model. J. Antimicrob. Chemother. 2016, 71, 1012–1021. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Sou, T.; Hansen, J.; Liepinsh, E.; Backlund, M.; Ercan, O.; Grinberga, S.; Cao, S.; Giachou, P.; Petersson, A.; Tomczak, M.; et al. Model-Informed Drug Development for Antimicrobials: Translational PK and PK/PD Modeling to Predict an Efficacious Human Dose for Apramycin. Clin. Pharmacol. Ther. 2021, 109, 1063–1073. [Google Scholar] [CrossRef]
- Iqbal, K.; Broeker, A.; Nowak, H.; Rahmel, T.; Nussbaumer-Pröll, A.; Österreicher, Z.; Zeitlinger, M.; Wicha, S.G. A pharmacometric approach to define target site-specific breakpoints for bacterial killing and resistance suppression integrating microdialysis, time–kill curves and heteroresistance data: A case study with moxifloxacin. Clin. Microbiol. Infection. 2020, 26, 1255.e1–1255.e8. [Google Scholar] [CrossRef]
- Lim, H.S.; Chong, Y.P.; Noh, Y.H.; Jung, J.A.; Kim, Y.S. Exploration of optimal dosing regimens of vancomycin in patients infected with methicillin-resistant Staphylococcus aureus by modeling and simulation. J. Clin. Pharm. Ther. 2014, 39, 196–203. [Google Scholar] [CrossRef]
- Lyons, M.A. Computational pharmacology of rifampin in mice: An application to dose optimization with conflicting objectives in tuberculosis treatment. J. Pharm. Pharmacodyn. 2014, 41, 613–623. [Google Scholar] [CrossRef]
- Lyons, M.A.; Lenaerts, A.J. Computational pharmacokinetics/pharmacodynamics of rifampin in a mouse tuberculosis infection model. J. Pharm. Pharmacodyn. 2015, 42, 375–389. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Goutelle, S.; Bourguignon, L.; Jelliffe, R.W.; Conte, J.E.; Maire, P. Mathematical modeling of pulmonary tuberculosis therapy: Insights from a prototype model with rifampin. J. Theor. Biol. 2011, 282, 80–92. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Scheerans, C.; Wicha, S.G.; Michael, J.; Derendorf, H.; Kloft, C. Concentration–response studies and modelling of the pharmacodynamics of linezolid: Staphylococcus aureus versus Enterococcus faecium. Int. J. Antimicrob. Agents. 2015, 45, 54–60. [Google Scholar] [CrossRef]
- Boisson, M.; Jacobs, M.; Grégoire, N.; Gobin, P.; Marchand, S.; Couet, W.; Mimoz, O. Comparison of Intrapulmonary and Systemic Pharmacokinetics of Colistin Methanesulfonate (CMS) and Colistin after Aerosol Delivery and Intravenous Administration of CMS in Critically Ill Patients. Antimicrob. Agents Chemother. 2014, 58, 7331–7339. [Google Scholar] [CrossRef] [Green Version]
- Aranzana-Climent, V.; Buyck, J.M.; Smani, Y.; Pachón-Diaz, J.; Marchand, S.; Couet, W.; Grégoire, N. Semi-mechanistic PK/PD modelling of combined polymyxin B and minocycline against a polymyxin-resistant strain of Acinetobacter baumannii. Clin. Microbiol. Infection 2020, 26, 1254.e9–1254.e15. [Google Scholar] [CrossRef] [Green Version]
- Bian, X.; Liu, X.; Chen, Y.; Chen, D.; Li, J.; Zhang, J. Dose Optimization of Colistin Combinations against Carbapenem-Resistant Acinetobacter baumannii from Patients with Hospital-Acquired Pneumonia in China by Using an In Vitro Pharmacokinetic/Pharmacodynamic Model. Antimicrob. Agents Chemother. 2019, 63, e01989-18. [Google Scholar] [CrossRef] [Green Version]
- Mohamed, A.F.; Kristoffersson, A.N.; Karvanen, M.; Nielsen, E.I.; Cars, O.; Friberg, L.E. Dynamic interaction of colistin and meropenem on a WT and a resistant strain of Pseudomonas aeruginosa as quantified in a PK/PD model. J. Antimicrob. Chemother. 2016, 71, 1279–1290. [Google Scholar] [CrossRef]
- Khan, D.D.; Lagerbäck, P.; Malmberg, C.; Kristoffersson, A.N.; Wistrand-Yuen, E.; Sha, C.; Cars, O.; Andersson, D.I.; Hughes, D.; Nielsen, E.I.; et al. Predicting mutant selection in competition experiments with ciprofloxacin-exposed Escherichia coli. Int. J. Antimicrob. Agents 2018, 51, 399–406. [Google Scholar] [CrossRef]
- Thabit, A.K.; Monogue, M.L.; Newman, J.V.; Nicolau, D.P. Assessment of in vivo efficacy of eravacycline against Enterobacteriaceae exhibiting various resistance mechanisms: A dose-ranging study and pharmacokinetic/pharmacodynamic analysis. Int. J. Antimicrob. Agents. 2018, 51, 727–732. [Google Scholar] [CrossRef]
- Li, Y.; Nguyen, M.H.; Cheng, S.; Schmidt, S.; Zhong, L.; Derendorf, H.; Clancy, C.J. A pharmacokinetic/pharmacodynamic mathematical model accurately describes the activity of voriconazole against Candida spp. in vitro. Int. J. Antimicrob. Agents. 2008, 31, 369–374. [Google Scholar] [CrossRef] [Green Version]
- Li, Y.; Nguyen, M.H.; Schmidt, S.; Zhong, L.; Derendorf, H.; Clancy, C.J. Pharmacokinetic/pharmacodynamic modelling and in vitro simulation of dynamic voriconazole–Candida interactions. Int. J. Antimicrob. Agents. 2009, 34, 240–245. [Google Scholar] [CrossRef] [PubMed]
- Wang, T.; Zhang, T.; Meng, T.; Li, Y.; Chen, L.; Yang, Q.; Dong, H.; Lei, J.E.; Chen, L.; Dong, Y. A strategy for designing voriconazole dosage regimens to prevent invasive pulmonary aspergillosis based on a cellular pharmacokinetics/pharmacodynamics model. J. Transl. Med. 2018, 16, 157. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Venisse, N.; Grégoire, N.; Marliat, M.; Couet, W. Mechanism-Based Pharmacokinetic-Pharmacodynamic Models of In Vitro Fungistatic and Fungicidal Effects against Candida albicans. Antimicrob. Agents Chemother. 2008, 52, 937–943. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Sy, S.K.; Zhuang, L.; Derendorf, H. Pharmacokinetics and pharmacodynamics in antibiotic dose optimization. Expert Opin. Drug Metab. Toxicol. 2016, 12, 93–114. [Google Scholar] [CrossRef] [PubMed]
- Mouton, J.W.; den Hollander, J.G. Killing of Pseudomonas aeruginosa during continuous and intermittent infusion of ceftazidime in an in vitro pharmacokinetic model. Antimicrob. Agents Chemother. 1994, 38, 931–936. [Google Scholar] [CrossRef] [Green Version]
- Wellington, K.; Curran, M.P. Cefditoren Pivoxil: A Review of its Use in the Treatment of Bacterial Infections. Drugs 2004, 64, 2597–2618. [Google Scholar] [CrossRef]
- Jain, A.; Utley, L.; Parr, T.R.; Zabawa, T.; Pucci, M.J. Tebipenem, the first oral carbapenem antibiotic. Expert Rev. Anti-Infect. Ther. 2018, 16, 513–522. [Google Scholar] [CrossRef]
- Sato, N.; Kijima, K.; Koresawa, T.; Mitomi, N.; Morita, J.; Suzuki, H.; Hayashi, H.; Shibasaki, S.; Kurosawa, T.; Totsuka, K. Population Pharmacokinetics of Tebipenem Pivoxil (ME1211), a Novel Oral Carbapenem Antibiotic, in Pediatric Patients with Otolaryngological Infection or Pneumonia. Drug Metab. Pharmacokinet. 2008, 23, 434–446. [Google Scholar] [CrossRef] [Green Version]
- Curello, J.; MacDougall, C. Beyond Susceptible and Resistant, Part II: Treatment of Infections Due to Gram-Negative Organisms Producing Extended-Spectrum β-Lactamases. J. Pediatric Pharmacol. Ther. 2014, 19, 156–164. [Google Scholar] [CrossRef]
- Sy, S.K.B.; Derendorf, H. Experimental design and modelling approach to evaluate efficacy of β-lactam/β-lactamase inhibitor combinations. Clin. Microbiol. Infection 2018, 24, 707–715. [Google Scholar] [CrossRef] [Green Version]
- Brill, M.J.E.; Kristoffersson, A.N.; Zhao, C.; Nielsen, E.I.; Friberg, L.E. Semi-mechanistic pharmacokinetic–pharmacodynamic modelling of antibiotic drug combinations. Clin. Microbiol. Infection 2018, 24, 697–706. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Lepak, A.J.; Andes, D.R. Antifungal Pharmacokinetics and Pharmacodynamics. Cold Spring Harb. Perspect. Med. 2015, 5, a019653. [Google Scholar] [CrossRef] [PubMed]
- Sale, M.; Sherer, E.A. A genetic algorithm based global search strategy for population pharmacokinetic/pharmacodynamic model selection: Genetic algorithm in PK/PD model selection. Br. J. Clin. Pharmacol. 2015, 79, 28–39. [Google Scholar] [CrossRef] [Green Version]
- Rayner, C.R.; Smith, P.F.; Andes, D.; Andrews, K.; Derendorf, H.; Friberg, L.E. Model-Informed Drug Development for Anti-Infectives: State of the Art and Future. Clin. Pharmacol. Ther. 2021, 109, 867–891. [Google Scholar] [CrossRef] [PubMed]
- He, S.; Cheng, Z.; Xie, F. Pharmacokinetic/pharmacodynamic-guided gentamicin dosing in critically ill patients: A revisit of the Hartford nomogram. Int. J. Antimicrob. Agents. 2022, 59, 106600. [Google Scholar] [CrossRef] [PubMed]
- Nichols, W.W.; Newell, P.; Critchley, I.A.; Riccobene, T.; Das, S. Avibactam Pharmacokinetic/Pharmacodynamic Targets. Antimicrob. Agents Chemother. 2018, 62, e02446-17. [Google Scholar] [CrossRef] [Green Version]
- Cojutti, P.G.; Carnelutti, A.; Lazzarotto, D.; Sozio, E.; Candoni, A.; Fanin, R.; Tascini, C.; Pea, F. Population Pharmacokinetics and Pharmacodynamic Target Attainment of Isavuconazole against Aspergillus fumigatus and Aspergillus flavus in Adult Patients with Invasive Fungal Diseases: Should Therapeutic Drug Monitoring for Isavuconazole Be Considered as Mandatory as for the Other Mold-Active Azoles? Pharmaceutics 2021, 13, 2099. [Google Scholar]
- Roberts, J.A.; Kirkpatrick, C.M.J.; Roberts, M.S.; Robertson, T.A.; Dalley, A.J.; Lipman, J. Meropenem dosing in critically ill patients with sepsis and without renal dysfunction: Intermittent bolus versus continuous administration? Monte Carlo dosing simulations and subcutaneous tissue distribution. J. Antimicrob. Chemother. 2009, 64, 142–150. [Google Scholar] [CrossRef] [Green Version]
- Iqbal, K.; Rohde, H.; Huang, J.; Tikiso, T.; Amann, L.F.; Zeitlinger, M.; Wicha, S.G. A pharmacokinetic-pharmacodynamic (PKPD) model-based analysis of tedizolid against enterococci using the hollow-fibre infection model. J. Antimicrob. Chemother. 2022, dkac183. [Google Scholar] [CrossRef]
Author | Drug | Class | PD Model | Bacteria/Fungal | Contribution of PK/PD Modeling |
---|---|---|---|---|---|
DE ARAUJO et al., 2011 [41] | Piperacillin | Beta-lactam—Penicillin | In vivo | Escherichia coli | To model the killing effect of piperacillin against Escherichia coli in immunocompromised infected rats. |
To compare the PK-PD parameters obtained in vivo with those determined by simulating in vitro against E. coli the free tissue levels of piperacillin expected at the infection site in humans. | |||||
BERGEN et al., 2017 [42] | Meropenem | Beta-lactam—Carbapenem | In vitro | Pseudomonas | To quantify and characterize the relationships between meropenem concentrations, bacterial killing, and regrowth over time for a wide range of studied renal functions and do-sing regimens. |
Aeruginosa | |||||
MATSUMOTO et al., 2014 [43] | Tebipenem pivoxil | Beta-lactam—Carbapenem | In vitro | Streptococcus pneumoniae | To predict the clinical bacteriological efficacy of antibiotics and examine the pharmacodynamics characteristics of antibiotics against bacterial strains. |
Haemophilus influenzae | |||||
MOUTON; VINKS; PUNT, 1997 [34] | Ceftazidime | Beta-lactam—Cephalosporin | In vitro | Pseudomonas aeruginosa | To characterize in vitro bacterial killing rate as a function of ceftazidime concentrations over time. |
de LA PEÑA et al., 2004[44] | Cefaclor | Beta-lactam—Cephalosporin | In vitro | Escherichia coli | To describe the PK/PD relationship of Cefaclor with an appropriate mathematical model and to simulate the pharmacodynamic effect of any given dose and dosing regimen on any of the bacterial strains. |
Moraxella catarrhalis | |||||
Haemophilus influenzae | |||||
Streptococcus pneumoniae | |||||
MATSUMOTO et al., 2014 [43] | Cefditoren pivoxil | Beta-lactam—Cephalosporin | In vitro | Streptococcus pneumoniae | To predict the clinical bacteriological efficacy of cefditoren pivoxil and to examine the pharmacodynamic characteristics of antibiotics against bacterial strains. |
Haemophilus influenzae | |||||
MOHAMED et al., 2011 [38] | Gentamicin | Aminoglycoside | In vitro | Escherichia coli | To develop a PK/PD model to describe the time course of the bactericidal activity of gentamicin against Escherichia coli. |
ZHUANG et al., 2015 [45] | Gentamicin | Aminoglycoside | In vitro | Staphylococcus aureus (MRSA) | To establish the posological regimen of Gentamicin for patients with ESRD. |
Staphylococcus aureus (MSSA) | |||||
Pseudomonas aeruginosa | |||||
SOU et al., 2021 [46] | Tobramycin | Aminoglycoside | In vivo | Pseudomonas aeruginosa | To characterize in a semi-mechanistic mathematical model in an attempt to provide a description of biofilm development and drug effects on bacteria in different states in vivo. |
To evaluate the effect of different dosing regimens with tobramycin | |||||
IQBAL et al., 2020 [47] | Moxifloxacin | Fluoroquinolone | In vitro | Staphylococcus aureus | To develop and evaluate a pharmacometrics approach integrating clinical PK data from (unbound) plasma and target tissues (muscle and skin) of fluoroquinolone moxifloxacin against Staphylococcus aureus and Escherichia coli in infected patients using microdialysis, as well as in vitro time-kill and resistance development. |
Escherichia coli | |||||
LIM et al., 2014 [48] | Vancomycin | Glycopeptide | In vitro | MRSA | To evaluate vancomycin’s pharmacokinetics (PK) and pharmacodynamics (PD) and explore its optimal dosing regimens by modeling and simulation. |
LYONS, 2014 [49] | Rifampicin | Rifamycin | In vitro | Mycobacterium tuberculosis | To quantitatively explore trade-offs between therapeutic and adverse effects of optimal dosing, such as rifampicin in TB-infected mice. |
LYONS; LENAERTS, 2015 [50] | Rifampicin | Rifamycin | In vivo | Mycobacterium tuberculosis | To simulate drug therapy’s PK/PD properties for experimental TB and determine the PK/PD index that best correlates with efficacy. |
GOUTELLE et al., 2011 [51] | Rifampicin | Rifamycin | In vivo | Mycobacterium | To set up a prototype mathematical model of TB treatment by rifampicin based on pharmacokinetics, pharmacodynamics, and disease submodels. |
Tuberculosis | |||||
TREYAPRASET et al., 2007 [35] | Azithromycin | Macrolide | In vitro | Streptococcus pneumoniae/penicillin-intermediate | To describe the PK/PD relationship of azithromycin against different strains. |
S. pneumoniae/penicillin-sensitive | |||||
Haemophilus influenzae | |||||
Moraxella catarrhalis | |||||
SCHEERANS et al., 2015 [52] | Linezolid | Oxazolidinone | In vitro | Staphylococcus aureus | To measure and compare the antibacterial effect of linezolid against S. aureus and E. faecium in a static in vitro infection model and characterize the underlying PK/PD relationship via a mathematical PK/PD model. |
Enterococcus faecium | |||||
BOISSON et al., 2014 [53] | Colistin | Polypeptide | In vitro | Pseudomonas aeruginosa | To assess the effect of the route of administration on the antimicrobial effect of colistin within the lung. |
ARANZANA-CLIMENT et al., 2020 [54] | Polymyxin B + Minocycline | Polypeptide + Tetracycline | In vitro | Acinetobacter baumannii | To develop a semi-mechanistic PK/PD model based on extensive in vitro time-kill experiments and determine the resistant bacterial count of Polymyxin B + Minocycline against Acinetobacter baumannii. |
BIAN et al., 2019 [55] | Colistin + Meropenem | Polypeptide + Beta-lactam—Carbapenem | In vitro | Acinetobacter baumannii | To develop a semi-mechanistic PK/PD model to optimize the colistin and meropenem combination against carbapenem-resistant Acinetobacter baumannii. |
MOHAMED et al., 2016 [56] | Colistin + Meropenem | Polypeptide + Beta-lactam—Carbapenem | In vitro | Pseudomonas aeruginosa | To develop a pharmacokinetic/pharmacodynamic (PK/PD) model that describes the in vitro bacterial time-kill curves of colistin and Meropenem alone and in combination for one wild-type and one Meropenem resistant strain of P. aeruginosa. |
KHAN et al., 2018 [57] | Ciprofloxacin | Quinolone | In vitro | Escherichia coli | To predict in vitro mixed-population experiments with competition between E. coli wild-type and three well-defined E. coli-resistant mutants when exposed to ciprofloxacin. |
THABIT et al., 2018 [58] | Eravacycline | Tetracycline | In vivo | Enterobacteriaceae | To assess the correlation of the ƒAUC/MIC index with the efficacy of eravacycline in an animal infection model and to determine its magnitude using Enterobacteriaceae. |
NIELSEN et al., 2007 [36] | Benzylpenicillin Cefuroxime Erythromycin Moxifloxacin Vancomycin | Beta-lactam—Penicillin Beta-lactam— Cefalosphorin Macrolide Fluoroquinolone Glycopeptide | In vitro | Streptococcus pyogenes | To develop a semimechanistic PK/PD model to evaluate the antibacterial activity of different drugs against Streptococcus pyogenes through a time-kill curve experiment. |
LI et al., 2008 [59] | Voriconazole | Azole | In vitro | Candida albicans | To develop a pharmacokinetic/pharmacodynamic (PK/PD) mathematical model that fits voriconazole time-kill data against Candida isolates in vitro and to use the model to simulate the expected kill curves for typical intravenous and oral dosing regimens. |
Candida glabrata | |||||
Candida parapsilosis | |||||
LI et al., 2009 [60] | Voriconazole | Azole | In vitro | Candida albicans | To fit dynamic time-kill data and simulate the expected kill curves in vivo. |
Candida glabrata | |||||
Candida parapsilosis | |||||
WANG et al., 2018 [61] | Voriconazole | Azole | In vitro | Aspergillus fumigatus | To identify a way to design an optimal prophylactic antifungal regimen through the cellular PK/PD model. |
VENISSE et al., 2008 [62] | Caspofungin | Echinocandins | In vitro | Candida albicans | To evaluate the fungicidal and fungistatic activity of caspofungin and fluconazole against Candida albicans. |
Fluconazole | Azole |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Pereira, L.C.; Fátima, M.A.d.; Santos, V.V.; Brandão, C.M.; Alves, I.A.; Azeredo, F.J. Pharmacokinetic/Pharmacodynamic Modeling and Application in Antibacterial and Antifungal Pharmacotherapy: A Narrative Review. Antibiotics 2022, 11, 986. https://doi.org/10.3390/antibiotics11080986
Pereira LC, Fátima MAd, Santos VV, Brandão CM, Alves IA, Azeredo FJ. Pharmacokinetic/Pharmacodynamic Modeling and Application in Antibacterial and Antifungal Pharmacotherapy: A Narrative Review. Antibiotics. 2022; 11(8):986. https://doi.org/10.3390/antibiotics11080986
Chicago/Turabian StylePereira, Laiz Campos, Marcelo Aguiar de Fátima, Valdeene Vieira Santos, Carolina Magalhães Brandão, Izabel Almeida Alves, and Francine Johansson Azeredo. 2022. "Pharmacokinetic/Pharmacodynamic Modeling and Application in Antibacterial and Antifungal Pharmacotherapy: A Narrative Review" Antibiotics 11, no. 8: 986. https://doi.org/10.3390/antibiotics11080986
APA StylePereira, L. C., Fátima, M. A. d., Santos, V. V., Brandão, C. M., Alves, I. A., & Azeredo, F. J. (2022). Pharmacokinetic/Pharmacodynamic Modeling and Application in Antibacterial and Antifungal Pharmacotherapy: A Narrative Review. Antibiotics, 11(8), 986. https://doi.org/10.3390/antibiotics11080986